Evolution, organization and economic behavior

Evolution, Organization and Economic
Behavior

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Evolution,
Organization and
Economic Behavior
Edited by

Guido Buenstorf
University of Kassel, Germany

Edward Elgar
Cheltenham, UK • Northampton, MA, USA

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© Guido Buenstorf 2012

All rights reserved. No part of this publication may be reproduced, stored in a
retrieval system or transmitted in any form or by any means, electronic,
mechanical or photocopying, recording, or otherwise without the prior
permission of the publisher.
Published by
Edward Elgar Publishing Limited
The Lypiatts
15 Lansdown Road
Cheltenham
Glos GL50 2JA
UK
Edward Elgar Publishing, Inc.
William Pratt House
9 Dewey Court
Northampton
Massachusetts 01060
USA

A catalogue record for this book
is available from the British Library
Library of Congress Control Number: 2012930578

ISBN 978 1 84980 628 2

02

Typeset by Servis Filmsetting Ltd, Stockport, Cheshire
Printed and bound by MPG Books Group, UK

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Contents
List of figures
List of tables
List of contributors
1

Introduction
Guido Buenstorf

PART I

2

vii
viii
ix
1

ECONOMIC BEHAVIOR: INDIVIDUALS AND
INTERACTIONS

To weigh or not to weigh, that is the question: advice on
weighing goods in a boundedly rational way
Werner Güth and Hartmut Kliemt

23

3

Emergent cultural phenomena and their cognitive foundations
Christian Cordes

4

Consumer learning through interaction: effects on aggregate
outcomes
Zakaria Babutsidze

58

Scientists’ valuation of open science and commercialization:
the influence of peers and organizational context
Stefan Krabel

75

5

PART II
6

7

8

39

THE EVOLUTION OF FIRMS

Capturing firm behavior in agent-based models of industry
evolution and macroeconomic dynamics
Herbert Dawid and Philipp Harting

103

The emergence of clan control in a science-based firm: the case
of Carl Zeiss
Markus C. Becker

131

Creativity, human resources and organizational learning
Thierry Burger-Helmchen and Patrick Llerena

155

v

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Evolution, organization and economic behavior

PART III

9

EVOLVING FIRMS AS DRIVERS OF ECONOMIC
DEVELOPMENT

Economic development as a branching process
Koen Frenken and Ron A. Boschma

185

10

Spin-off growth and job creation: evidence on Denmark
Pernille Gjerløv-Juel and Michael S. Dahl

197

11

Innovationes Jenenses: some insights into the making of a
hidden star
Uwe Cantner

Index

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Figures
2.1 Aspiration profiles in a simple portfolio decision
3.1 Spreading of a novel trait in a population via processes of
biased cultural transmission for different values of a
(denoted by ‘a’ in the figure) and b
4.1 Dependence of returns to advertising on user friendliness
and communication intensity
4.2 Cross-sectional cuts into Figure 4.1
5.1 Distribution of open science identity
5.2 Distribution of perceived reputational reward from
commercialization
6.1 Dynamics of per capita output averaged over 10 runs for
different values of the stock-out probability
6.2 Dynamics of per capita output averaged over 10 runs for
different values of the markup
6.3 Dynamics of the markup charged by firms in a single
simulation run
6.4 Dynamics of per capita output averaged over 10 runs
8.1 Categories of knowledge assets, value creation and value
capture
8.2 Value creation linchpin from a management perspective
8.3 Division of labor and division of knowledge balance
10.1 Median and 75th percentile of number of full-time

equivalents of spin-offs and other entrants, classified by age
10.2 Job creation and job destruction of spin-offs and other
entrants, classified by age
10.3 Boxplox of employment growth, classified as spin-offs and
others
10.4 Kaplan-Meier survival rates for spin-offs and other entrants
11.1 Regional patent output
11.2 Cooperation potentials
11.3 Realized cooperations

31

52
68
70
87
87
113
114
116
117
158
174
176
208
209
210
212
225
227

229

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Tables
2.1
3.1

Room for advice in different decision contexts
Probability of an agent acquiring Trait 1 or Trait 2 given a
particular model and the cultural variant held by the agent
5.1
Correlation matrix
5.2
Ordered probit and probit analysis of scientists’ open
science identity
5.3
Ordered probit and probit analysis of scientists’ perceived
reputational reward from commercializing research
6A.1 Parameter settings
8.1
Learning forms and creativity development factors
8.2
Management modes of division of labor and knowledge
9.1

Possible events resulting from a product innovation
10.1
Average firm size for spin-offs and other entrants,
classified by age
10.2
Share of firms with positive, negative and zero growth,
respectively, of spin-offs and other entrants, classified by
age
10.3
Job creation and job destruction of spin-offs and other
entrants, classified by age
10.4
Share of job creation and job destrucion of spin-offs and
other entrants, classified by age
10.5
Yearly average of total employment for spin-offs and
other entrants, classified by age
10.6
Total employment per 100 start-ups
11.1
Descriptive statistics of the networks of technological
overlap
11.2
Descriptive statistics of the interpersonal networks
11.3
Network regression
11.4
Cooperation: mean degree
11.5
Scientist mobility: mean degree

11.6
Internal and external relations in the network
11.7
Cooperation network and a firm’s innovative capacity

37
50
86
89
93
122
172
177
188
207

210
211
214
215
216
227
229
232
235
236
237
238

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Contributors
Zakaria Babutsidze is Assistant Professor at the Department of
Economics, SKEMA Business School, and Economist at the Department
of Innovation and Competition, Observatoire Français des Conjonctures
Économiques, Sciences-Po Paris, France.
Markus C. Becker is Professor at the Strategic Organization Design Unit,
Department of Marketing and Management, University of Southern
Denmark.
Ron A. Boschma is Professor of Regional Economics and Director of the
Urban and Regional Research Centre Utrecht, Utrecht University, the
Netherlands. He is also affiliated with the Spatial Economics Research
Centre of the London School of Economics, United Kingdom.
Guido Buenstorf is Professor of Economics at the University of Kassel,
Germany.
Thierry Burger-Helmchen is Professor of Management Science at EM
Strasbourg, University of Strasbourg, and member of the Bureau
d’Économie Théorique et Appliquée research centre, University of
Strasbourg, France.
Uwe Cantner is Professor of Economics at the Friedrich Schiller University
of Jena, Germany, and the University of Southern Denmark, Odense,
Denmark.
Christian Cordes is Professor of Economics at the University of Bremen,
Germany.
Michael S. Dahl is Professor of the Economics of Entrepreneurship and

Organizations at the Department of Business and Management, Aalborg
University, Denmark.
Herbert Dawid is Professor at the Department of Business Administration
and Economics and the Institute of Mathematical Economics, University
of Bielefeld, Germany.
Koen Frenken is Professor in Economics of Innovation and Technological
Change, Eindhoven Centre for Innovation Studies, Eindhoven University
ix

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Evolution, organization and economic behavior

of Technology, and a Fellow in Economic Geography at the Urban and
Regional Research Centre Utrecht, Utrecht University, the Netherlands.
Pernille Gjerløv-Juel is a PhD Fellow at the Department of Business and
Management, Aalborg University, Denmark.
Werner Güth is Director of the Strategic Interaction Group, Max Planck
Institute of Economics, Jena, Germany.
Philipp Harting is a PhD student at the Department of Business
Administration and Economics, University of Bielefeld, Germany.
Hartmut Kliemt is Professor of Philosophy and Economics at the Frankfurt
School of Finance and Management, Germany.
Stefan Krabel is a Research Associate at the Institute of Economics,
University of Kassel, Germany.

Patrick Llerena is Professor at the Bureau d’Économie Théorique and
Appliquée, University of Strasbourg, and Directeur Général at Fondation
Université de Strasbourg, France.

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1.

Introduction
Guido Buenstorf

1

EVOLUTIONARY ECONOMICS AS BEHAVIORAL
ECONOMICS OF INDIVIDUALS AND
ORGANIZATIONS

Evolutionary economics is behavioral economics – it studies the behavior of individuals and organizations in economic contexts. Evolutionary
economists share parts of their research agenda with (other) behavioral
economists and with management scholars, and the more the three communities interact, the more they can each learn from the others. These
three premises underlie the present collection.
That evolutionary economics is behavioral economics is by no means a
new idea, but has been proposed – and practiced – for more than 100 years
before these lines were written. The starting point of evolutionary economics can be dated to 1898, the year when Thorstein Veblen posed his famous
question: ‘Why is economics not an evolutionary science?’. Veblen’s programmatic essay chastised economics for not engaging in a causal analysis
of economic processes, which to him was the essence of evolutionary
thinking (Veblen, 1898; see also Hodgson, 1998). To do so presupposed

studying the behavior of human agents, who after all are ‘the motor forces
of the processes of economic development’ (Veblen, 1898, p. 388).
For Veblen, the rational choice approach of neoclassical economics
emerging at the time – portraying human agents as ‘lightning calculators
of pleasures and pains’, as he sardonically put it (p. 389) – would not
do the job. His alternative vision of economic agency was derived from
contemporary instinct psychology (see Cordes, 2007, and the references
therein). In this view, the individual agent’s desires, thoughts and behavioral dispositions are not exogenously given and permanently fixed, but
are the dynamic ‘products of his hereditary traits and his past experience’
(Veblen, 1989, p. 390). Thus, to understand the behavior of human agents
we have to ask how they became what they are. And to answer this question both individual learning experiences and the phylogenic processes
that shaped human nature in the species’ evolutionary history need to be

1

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taken into account. This perspective, even though developed in the late
nineteenth century, comes very close to contemporary views in evolutionary economics.
Understanding human agents as products of their past, moreover,
leaves room for interaction with and learning from other agents. For
Veblen, economic agents are not isolated decision makers. Their goaloriented activities unfold in the context of their cultural environment,
which is in turn modified through the activities of individual agents. Firms

are a relevant part of the cultural context shaping (and being shaped by)
individual behavior. It is therefore not surprising that Veblen’s perspective
on economic agency provided the theoretical foundation on which other
American institutionalists built their critique of labor relations and corporate cultures prevailing in US corporations at the time (Cordes, 2007).
Joseph Schumpeter (1934; first published in German in 1911), the
other founding father of evolutionary economics, likewise saw the need
to understand human behavior to make sense of economic development.
His innovative entrepreneur – the driver of Schumpeterian economic
development – is a multifaceted individual. The entrepreneur’s complex
motivational structure could hardly be more different from the utilitarian
agent of the rational choice approach who optimizes an income–leisure
trade-off. It is not the expectation to make a profit from successful innovation that motivates Schumpeter’s entrepreneur, and he (sic!) does not
calculate a return on investment. Rather, he is restless and driven by the
will to achieve, to win against his competitors, and to ‘found a private
kingdom’ (Schumpeter, 1934, p. 93).
Nonetheless, as Witt (2002) has succinctly pointed out, in Schumpeter’s
(1934) elitist theory of economic development all interesting behavioral
facets are limited to the entrepreneur. Both competing ‘mere managers’
and consumers are portrayed as passive, and much of the challenge faced
by the entrepreneur is to overcome the obstacles posed by a social environment hostile to innovations. Thus, the behavioral perspective is clearly
present in the younger Schumpeter, yet it remains sketchy and far from
being fully explored. Furthermore, the process of invention is not part of
Schumpeter’s (1934) theory. Schumpeterian entrepreneurs are innovators,
not inventors. Their creative activity is limited to discovering the economic
opportunities provided by ideas that already pre-exist outside the market
sphere. The Schumpeterian entrepreneur is likewise not necessarily a
capitalist, firm owner or manager. As a consequence, how entrepreneurial
ideas are realized in business firms is not part of Schumpeter’s theory,
either.
As is well known, firms came to play a dominant role in Schumpeter’s

later theorizing. Following the historical ascent of the industrial research

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Introduction

3

and development (R&D) laboratory, in Capitalism, Socialism and
Democracy, Schumpeter (1942) argued that the locus of innovation had
mostly shifted from individual entrepreneurs to large corporations. A
less noticed change in Schumpeter’s thought relates to the motivation
underlying innovative activities. He now portrayed business firms as profit
oriented, and while they were not argued to be profit maximizing, they
require the prospect of monopoly profits to be induced to engage in innovative activities. Otherwise, in spite of its important role in Capitalism,
Socialism and Democracy, the firm essentially remained a black box in
Schumpeter’s later work.
This is very different in Richard Nelson and Sidney Winter’s seminal
Evolutionary Theory of Economic Change (1982), which started the modern
tradition of evolutionary economics. Nelson and Winter’s conception of
evolutionary economics focuses on competition in innovative markets. As
a consequence, firms are at the center of interest. Taking up ideas developed in the Carnegie tradition (for example, March and Simon, 1958;
Cyert and March, 1963), the rule-based character of firms’ decision making
is stressed. As Winter (1971, p. 239) had put it in an earlier article, ‘firms
establish decision rules and apply them routinely over extended periods’.
These organizational routines, which also encompass the ‘relatively constant dispositions and strategic heuristics that shape the approach of a
firm to the nonroutine problems it faces’ (Nelson and Winter, 1982, p. 15),

are at the heart of Nelson and Winter’s theory of the firm.
Nelson and Winter thus shift the focus of evolutionary economics
from individual to organizational behavior, which is of primary relevance
to explain competition in markets. To be sure, the book does contain
an entire chapter dedicated to a discussion of individual skills, drawing
extensively on insights from cognitive psychology. However, individual
skills are seen as an analogue to organizational routines, and they are
mainly discussed to motivate the subsequent discussion of how partially
tacit organizational routines underlie the coordinated performance of firm
organizations.
The routine concept also provides the link to the selection concept borrowed from biology. Winter (1971) had already argued that selection is
a meaningful concept in industrial economics only if some dimension of
firm activity can be found that is long-lived enough for selection to operate
upon, and suggested firms’ decision rules as a suitable candidate for such
a concept. In Nelson and Winter (1982), the routine-based theory of
organizational behavior is combined with agent-based simulation models
to develop an alternative growth theory based on competing heterogeneous firms operating in innovative markets. Characteristic of their evolutionary model is that the development of firms (and, as a consequence,

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industries) is informed by their past: ‘[t]he condition of the industry in
each time period bears the seeds of its condition in the following period’
(ibid., p. 19). Continuity in firm behavior is further enhanced by assuming

satisficing behavior of firms. Firms’ primary objective is to preserve their
routines, and they only search for improved processes when profits fall
below their aspiration level.
The analogy between market competition and natural selection is
prominent in the Nelson–Winter book, and there is liberal use of biological rhetoric (such as characterizing ‘routines as genes’; p. 134). At the same
time, the authors explicitly disavow the idea that market competition is
driven by precisely the same processes as natural selection, or even the
objective to develop a more general theory of evolution. Likewise, they
make no attempt to link presently observable human behavior to past
evolutionary processes shaping human nature during the phylogeny of the
human species. This is a striking difference to Veblen’s vision of evolutionary economics, but also – as will be seen immediately – to another strand
of modern evolutionary economics.
It was only a few years after Nelson and Winter (1982) that an independent line of evolutionary economic thought was started in Germany
by Ulrich Witt. In a book that was never translated into English, Witt
(1987) endeavored to provide individualistic foundations of evolutionary economics. Conceptualizing evolutionary economics as a general
approach to the study of emergent novelty and dynamic change in the
economy, Witt set out an ambitious agenda that went far beyond industrial economics. Underlying his approach are two premises. First, that
the scattered evolutionary ideas in economics (in addition to Schumpeter,
the Nelson–Winter approach, as well as some contributions by Germanspeaking authors, Witt discusses at length the (neo-)Austrian school of
Friedrich von Hayek, Israel Kirzner and others) lack a unifying theoretical
foundation. And second, that this foundation needs to be provided by a
sufficiently general theory of individual behavior and human interaction.
Evolutionary biology is highly relevant in this perspective of evolutionary
economics – not to provide a template for evolutionary processes in the
economic sphere, but because the behavioral dispositions and cognitive
capacities of contemporary agents were shaped during human phylogeny.
Witt’s general conception of evolutionary economics thus comes closer
to that developed by Veblen than either to Schumpeter’s or Nelson and
Winter’s views.
Where Nelson and Winter (1982) drew their primary inspiration from

organizational theory, Witt (1987) is heavily influenced by behaviorist and
cognitive psychology. He develops a rich account of individual economic
agency based on inherited traits as well as a variety of learning processes.

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Introduction

5

For example, in the field of consumer behavior, Witt proposes that
humans share a universal set of innate wants and needs. (These ideas were
later elaborated in Witt, 2001.) This genetic endowment, however, is only
a starting point for the acquisition of new desires as well as increasingly
sophisticated ways of satisfying them. Learning results from individual
experience but also from vicarious learning based on observing the behavior of role models (Bandura, 1986). Learning from role models allows for
some individuals to have a disproportionately strong impact on how views
in their community develop.
In Witt’s behavioral model, idiosyncratic individual framing of the
environment and the human capacity to learn from observing others are
linked to provide an account of self-reinforcing dynamics of convergent
worldviews in communicating communities, and likewise increasingly
different worldviews between groups that do not communicate. In several
contributions, Witt (1998, 1999, 2000) applies his general behavioral
model to the theory of the firm. He suggests that successful founders
act as role models. In face-to-face interaction, they are able to shape the
worldviews of employees and effectively diffuse their own view of the

firm’s mission and strategy in the firm. This process of ‘cognitive leadership’ helps ensure coordinated activities in the firm. To the extent that the
entrepreneur’s views are internalized by employees, it also helps to keep
opportunistic behavior at bay. As the firm grows, face-to-face interaction
becomes increasingly difficult to sustain for the entrepreneur. This provides an endogenous cause of firm development, which may take one of
several directions: increased monitoring and formalization of procedures
(routines), delegation of leadership functions to second- and third-tier
managers, or even spin-off activities by disenchanted employees.
These ideas may be interpreted as providing an – admittedly indirect
– link from Schumpeter’s entrepreneur to Nelson and Winter’s view of
organizations. If Schumpeterian entrepreneurs establish firms to pursue
their innovative ideas, they may imprint their own ideas on employees.
As entrepreneurial visions differ, so will the worldviews of employees and,
as a consequence, the organizational routines emerging in the firm. Witt’s
cognitive leadership may thus help account for the heterogeneity of firm
routines and capabilities that is observable in many industries.
Since the 1980s, accounting for individual behavior in various contexts
has been a core subject in evolutionary economics. In particular, numerous
authors have focused on the learning of human agents and incorporated
learning into the analysis of various economic processes (see, for example,
Dosi et al., 2005, for a survey). In this way, our understanding of an
important aspect of economic behavior has been enhanced substantially.
Learning is a natural candidate as a focal point of evolutionary economics,

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Evolution, organization and economic behavior

as it is inherently a dynamic process unfolding over time. To the extent
that learning is done deliberately in order to attain some objective, learning moreover links the process perspective of evolutionary economics to
the intentional character of most economic activities. Learning agents may
be forward looking, and an evolutionary economics focusing on learning
is not restricted to modeling individual or organizational agents as passive
objects of (however specified) selection processes.
The evolutionary analysis of learning has often been linked back to
conceptual research originating outside economics. One important link is
provided by the work on genetic algorithms and evolutionary strategies
modeled after principles of evolutionary processes in nature. These algorithms were originally developed as applied (normative) problem-solving
devices, but they have also informed a (positive) theoretical literature
on learning in economics (see Brenner, 1998, for a critical discussion).
Another link is with complexity theory, notably the notion of adaptive
search in fitness landscapes (Kauffman, 1993). For example, building on
Herbert Simon’s (1962) notion of nearly decomposable systems, Frenken
et al. (1999) show that satisficing may be superior to optimization in
complex search spaces when search time is a relevant performance parameter (which is certainly plausible in innovative markets). Finally, learning
is a key driver of the dynamics in the agent-based computational modeling approach, which has been found to be well-suited for evolutionary
economics. Learning can relate to the actions taken by agents according
to some specified decision rules, or it can relate to the parameters or even
structure of these rules (Dawid and Harting, ch. 6, this volume).

2

NEW OPPORTUNITIES FOR INTERACTION AND
MUTUAL LEARNING

Since the foundations of the modern approach to evolutionary economics were developed in the 1980s, there have been various developments in

economics more generally that open new opportunities for fruitful interaction between evolutionary and ‘other’ economists. Most importantly, with
the ascent of laboratory experiments as a socially acceptable empirical
practice in economics, behavioral economics has gained increasing traction in the broader discipline. Numerous results have been established in
the lab that are hard to reconcile with the rationality axioms of standard
economists or at least with the generally accepted notions of what rational
decision making implies (see Wilkinson, 2007). The perspective on economic agency that informs at least some of the contributions to this line of
research is quite congenial to evolutionary economics.

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Introduction

7

Not only have economists outside the evolutionary camp rediscovered an
interest in actual human behavior, going beyond axiomatic rational choice
and taking seriously findings from other social sciences such as psychology.
Economists have also begun to link human behavior to the inherited nature
of the human cognitive apparatus. The most visible development in this
context is neuroeconomics (see Camerer et al., 2005). Based on their interdisciplinary interaction with neuroscience, economists have come to accept
that human behavior is multidimensional, and that, for instance, decisions
may vastly differ according to whether they are made by automatic or
controlled processes, or with different involvement of the brain’s affective
system. These insights lead at least some economists to conclude that ‘[t]he
way the brain evolved is critical to understanding human behavior’ (ibid.,
p. 25). And even in Chicago, economists have realized that when real-world
agents fail to conform to the predictions of the rational choice model, the

policy implications of that model may not be appropriate in a variety of
real-world contexts (Thaler and Sunstein, 2003).
In this way, more than 100 years after Veblen’s (1898) programmatic
essay, economics may eventually be on its way to becoming an evolutionary science. En route there is still much that evolutionary economics has
to offer to the rest of the discipline. Organizational behavior tends to be
underexplored by behavioral economists, and likewise the issue of how
complex macro-level patterns emerge from the micro-level interaction of
individual agents. Evolutionary economics has a long and rich tradition
of empirical studies of how firms, markets and industries develop. The
body of empirical insights thus developed will be an important ingredient
toward a better understanding of organizational behavior, which is more
difficult to replicate in the lab than individual decision making. Closely
related – and equally important to understand industrial dynamics – is the
evolutionary research on innovation based on the insight that real-world
knowledge is generally not a public good that can be costlessly replicated.
On the demand side of markets, the recent upswing of behavioral economics has yet offered little in terms of an improved theory of consumer
behavior. Similar to innovation, here is a real opportunity for evolutionary economics to have an impact on how economics develops in the future.
At the same time, behavioral economists are working on ideas that are
highly relevant to evolutionary economics – such as the attempt to develop
a general (and mathematical) theory of satisficing, a potentially powerful
challenge to the engrained rational choice model of standard economics.
In the same vein, the empirical work on subjective well-being not only
challenges conventional welfare economics. It may also integrate insights
into the substance and dynamics of evolved human needs and desires into
notions of economic welfare.

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Evolutionary economics has likewise much to learn from management
scholars with their wealth of in-depth empirical knowledge about firms.
In this context, a lively discussion and further refinement of the routine
concept began at the intersection of evolutionary economics and the management literature (see, for example, Cohen and Bacdayan, 1994; Dosi et
al., 2000; Becker, 2004; Becker et al., 2005). In particular, routines have
been suggested as important repositories of organizational capabilities.
The capability-based view of the firm emphasizes strategic capabilities,
that is, capacities to use the firm’s resources that are tied to a customer
need, unique to the firm and hard to imitate by competitors (Teece and
Pisano, 1994). As the knowledge base underlying organizational routines
is partially tacit and not fully available to any single individual member of
the firm, capabilities residing in organizational routines are often strategic
in this sense.
In studying firm organizations, the routine concept is not the only contribution that evolutionary economists can bring to the debate. As noted
in the previous section, there has been a long interest in evolutionary economics in the personality of the entrepreneur, and also in the cognitive and
motivational dynamics within developing firms. In the field of industrial
dynamics, evolutionary economics has made key theoretical and empirical
contributions (Klepper, 1996, 2002; Klepper and Thompson, 2006). Again
these interests resonate with recent developments outside evolutionary
economics. For instance, entrepreneurship has emerged as a dynamic field
of research that shares both its Schumpeterian roots and many specific
research topics with evolutionary economics. Various contributions to
entrepreneurship research are moreover informed by behavioral economics (for example, the work on entrepreneurial overoptimism by Lowe and
Ziedonis, 2006), or even have a distinctly evolutionary flavor (such as
the work on genetic foundations of entrepreneurship by Nicolaou et al.,

2008). Both behavioral economists and management scholars are keenly
interested in motivational aspects of labor relations and their repercussions on individual effort and firm performance (for example, Fehr and
Schmidt, 2004, 2007; Sauermann and Cohen, 2010). Management scholars
have also adopted concepts and methods from the evolutionary work on
industrial dynamics. All these recent developments provide rich opportunities to enhance the state of knowledge in the economics of the firm. This
seems all the more important not only because most economists outside
the evolutionary community have so far shunned the evolutionary perspective on firms, but because economics has generally shown little interest
in how firms actually behave and decide. That the field of management
research has traditionally been open to many different theoretical inputs
may make the dialogue even more fruitful.

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Introduction

9

This volume is intended as a contribution to the tripartite communication between evolutionary economists, behavioral economists, and management scholars. Each of the individual chapters of the volume takes up
one or several aspects of the overlapping research agendas. Earlier versions
of many of the chapters were presented at the 2009 European Meeting on
Applied Evolutionary Economics (EMAEE) in the German city of Jena.
To dedicate the 2009 EMAEE conference in Jena to this communication
between the three communities was a straightforward choice for historical as well as present-day reasons. Historically, as is highlighted in both
Markus C. Becker’s and Uwe Cantner’s chapters, Jena experienced a
managerial natural experiment in the late nineteenth and early twentieth
centuries that transformed the city into one of the world’s earliest regional
innovation systems. At the core of this experiment was Ernst Abbe, a

scientist, Schumpeterian entrepreneur, philanthropist and architect of
close interaction between public research and private-sector innovation
at the regional level. Abbe’s own management principles are surprisingly close to the evolutionary view of firm organizations (Buenstorf
and Murmann, 2005). More recently, the Jena economics community
formed by the Friedrich Schiller University and the Max Planck Institute
of Economics has engaged in the close interaction between evolutionary
economics, behavioral economics, and the economics of entrepreneurship
and innovation.

3

PART I: ECONOMIC BEHAVIOR: INDIVIDUALS
AND INTERACTIONS

The present volume is organized into three parts. The first part consists of
four chapters focusing on individual agents and their interactions. Werner
Güth and Hartmut Kliemt (Chapter 2) set the stage with a discussion of
boundedly rational decision making under uncertainty. Their conceptual
chapter is one element of a broader research agenda that aims to provide
a mathematical formulation of satisficing behavior in the Simonian tradition (see, for example, Güth, 2007, 2010). Güth and Kliemt suggest
that when deciding under uncertainty, real-world agents do not normally
maximize their expected utility based on assigning probabilities to the possible states of the world they may encounter. Instead, agents selectively
focus their attention on specific scenarios about what may happen (including other agents’ decisions) and form aspiration levels of what outcomes
would be satisfactory in the selected scenarios. In this framework, a choice
option is ‘optimal’ if there is no alternative option that yields higher utility
under one of the scenarios considered by the agent while yielding at least

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the same utility under all other scenarios. Optimal aspirations profiles are
such that they can just be satisfied by the respective choice option in all
selected scenarios.
In the Güth–Kliemt framework, boundedly rational agents do not
make decisions that are dominated by alternatives in the set of considered
scenarios. However, bounded rationality will not normally lead agents to
choose the optimal decision (among all possible ones), but only a satisficing one based on the scenarios taken into consideration. The authors also
argue that preferences cannot be deduced from observable actions. In their
view, economics as a discipline needs to experience a ‘cognitive turn’ like
the one made decades ago by psychology, and develop theories about how
preferences are actually formed by human agents. Finally, they employ
the abstract framework developed in the chapter to delineate conditions
under which external advice may help boundedly rational decision makers
to arrive at improved decisions.
Seeking ‘a middle ground between the methodological individualism
of many social sciences and methodological collectivism’ (pp.  39–40),
Christian Cordes (Chapter 3) shifts the focus from the individual agent to
the interaction of many agents. Cordes argues that cultural phenomena are
emergent macro-level phenomena resulting from micro-level interaction,
while they in turn shape the behavior of these interacting agents. Insights
from anthropology provide the theoretical foundation of the chapter.
Cordes emphasizes humans’ unique capacity to understand others as
intentional agents, which underlies our social learning abilities, as the key
micro-level foundation of culture. Drawing on dual inheritance theory, he

suggests that a bias toward cooperative behavior was established in the
human psychological setup during a period of gene–culture coevolution.
In addition to this ‘direct’ bias in favor of cooperation, human nature is
characterized by other biases that helped boundedly rational agents make
functional decisions during human phylogeny. Specifically, anthropologists have identified biases toward conformist behavior and the imitation
of successful role models.
Based on the theoretical considerations about evolutionary influences
of human behavior, Cordes then develops a model of how new cultural
traits can diffuse in a population of interacting agents. The model provides
a coherent behavioral foundation for the logistic diffusion pattern that is
well known from empirical diffusion studies, yet difficult to reconcile with
sequences of purely individual rational choices from a set of given alternatives. In a broader context, models of the same basic structure – starting
at the level of individual behavior but also incorporating the reverse causality from population-level processes back to the individual level – are
suggested as fruitful tools for understanding social phenomena.

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The consumption models developed by Zakaria Babutsidze in Chapter
4 are of the type advocated by Cordes. The main objective of the chapter
is to alert readers to the importance of interaction among agents in studying consumer behavior: as personal communication and peer effects are
important drivers of consumer decision making, neglecting interaction
patterns in consumption may lead to false inferences about aggregate
demand patterns and also to erroneous policy (or business strategy)

recommendations.
Babutsidze uses two stylized model contexts to illustrate this point. In
a model of global interaction resulting in frequency-dependent adoption
patterns (analogous to the conformity bias in Cordes’s model in Chapter
3), accounting for interaction leads to dramatic changes in the long-run
market shares of the competing products. In contrast, in Babutsidze’s
second model local interaction is shown to affect the model’s transitional dynamics. Consumers in this model are assumed to acquire skills
enhancing their valuation of the consumed good (as is predicted by
Witt’s (2001) approach to consumer behavior). Consumer skills derive
both from the agents’ own experiential learning and from their interaction with neighboring agents (who may consume a different variety of
good). Babutsidze shows that the effectiveness of advertising for one
variety depends on the relative ease with which consumers can acquire
skills relevant to the product (its ‘user friendliness’). More importantly,
how user friendliness relates to the effectiveness of advertising is determined by the strength of interaction. Without social learning, advertising is most effective if products are similar. With local interaction,
effectiveness is enhanced by product heterogeneity in user friendliness.
Interestingly, this effect is stronger for intermediate than for extreme
levels of interaction. Babutsidze suggests that since learning is always
a dynamic process unfolding over time, effects such as those shown in
his exemplary models should be of particular interest to evolutionary
economists.
The first part of the book is concluded by an empirical chapter by
Stefan Krabel (Chapter 5) that turns to norms and attitudes in public
research. This is a particularly well-suited context to study the evolution of
agents‘ behavior, as norms in public research have changed considerably
over the past decades. Traditional standards of ‘open science’ (Merton,
1968; Dasgupta and David, 1984) have increasingly been challenged by
new demands on researchers and universities to prove their relevance by
producing results that can be commercialized. Under these conditions,
the chapter asks whether individual age, peer effects and organizational
tradition have empirically measurable relationships to individual norms

and incentives perceived by scientists. In other words: has the focus on

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technology transfer and ‘entrepreneurial universities’ affected the way
researchers think about science and their own role as scientists?
To answer this question, Krabel presents results from a survey of
researchers working at the Max Planck Society, Germany’s foremost
public research organization dedicated to basic research. The chapter thus
differs from the thrust of the empirical work on university technology
transfer, which has primarily been based on patent and publication data –
and has therefore been unable to detect changes in attitudes and perceived
incentives. By focusing on a single basic research-oriented organization,
it is moreover able to control institutional differences (for example, stemming from differences in technology transfer strategies), doing so in a
context where researchers are under comparatively little organizational
pressure to partake in commercialization activities. The empirical findings
indicate that a substantial shift in norms and perceived incentives may
indeed be underway. Older researchers are more likely to subscribe to the
norms of ‘open science’, and perceive systematically lower reputational
effects of commercialization, than their younger peers. A candidate for a
(partial) explanation is also provided, as individual attitudes closely reflect
those of peers working in the same institute.

4

PART II: THE EVOLUTION OF FIRMS

The three chapters of Part II deal with the behavior and development of
firm organizations. It is here that evolutionary economists can most fruitfully interact with management scholars. Indeed all three chapters – each
in its own original way – apply ideas and concepts from management to
the evolutionary analysis of firms.
As noted in Section 1 above, agent-based computational modeling
has been a prominent modeling approach in evolutionary studies of
industries and entire economies ever since Nelson and Winter (1982).
The strength of this approach – being able to model rich behavioral
dynamics of a variety of heterogeneous, interacting agents – has also
resulted in a key challenge: how to select decision rules for the agents
that are grounded in well-established behavioral findings, and yet come
up with a model whose dynamics are reasonably transparent? Herbert
Dawid and Philipp Harting (Chapter 6) suggest that a consensus on
this question would improve the ‘external’ usefulness of agent-based
models for practical applications. It would also enhance the ‘internal’
validity of results by facilitating robustness tests and the comparison of
results from alternative models. The authors propose a ‘management
science approach’ to the modeling of firm behavior. The essence of this

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approach is to implement ‘relatively simple decision rules that match
standard procedures of real-world firms as described in the corresponding management literature’ (p. 109). The approach is illustrated by the
assumptions about production and pricing decisions employed in a large
agent-based macro model that the authors co-developed in a collaborative research project.
The similarity of Dawid and Harting’s management science approach
and the original approach taken by Nelson and Winter (1982) is striking.
After all, the notion of organizational routines originated from the prior
findings established in the Carnegie tradition that firms tend to rely on
stable and often quite straightforward heuristics to deal with recurrent
tasks and problems. Against this backdrop, the contribution made by
Dawid and Harting is to show how more complex agent-based models can
be based along the same principles, and that well-established heuristics
and decision rules are available from management science. In model building, these may be used for modeling firm behavior just as experimental
findings can be used for modeling individual behavior.
‘Clan control’ (Ouchi, 1979) is a management approach to solve the
ubiquitous problem of organizational control, that is, to ensure that the
actual decisions made in a firm conform to the objectives of the firm’s top
management. Clan control attempts to align the objectives of managers
and employees based on commitment, traditions, socialization and stable
membership. It is well-suited to manage R&D operations because individual output is difficult to measure and, given the inherently uncertain
character of R&D, specific behaviors to attain the organization’s objectives cannot be prescribed.
But how does clan control actually become established in a firm? This
question, which is obviously highly relevant to understand firm development, is center-stage in the contribution by Markus C. Becker (Chapter
7). Becker traces the emergence of clan control in the empirical context
of Carl Zeiss, which pioneered microscope making in the nineteenth
century and has remained one of Germany’s most prominent firms in the
optical industry to this day. He shows that the firm’s management – most
importantly Ernst Abbe – employed a variety of means to install a control

system consistent with the notion of clan control. Commitment was fostered by mutual agreement on the relevance of precision; science-based
product innovation was turned into a tradition (and subsequently codified
in binding statutes prescribing detailed management principles for the
Zeiss firm); firm members were socialized in master–apprentice relationships based on intense face-to-face communication; and stable membership was fostered by labor relations aiming at long-term (frequently,
lifelong) employment.

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In the broader theoretical context, clan control can be understood as a
specific type of higher-level organizational routine (a strategic heuristic).
The Zeiss example indicates that organizational control systems develop
over time in a firm organization and have a lasting character. Becker’s
chapter can thus be read as an empirical case study of how routines emerge
in the firm. At the same time, the chapter contributes to the crucial yet
underresearched topic of how entrepreneurial ventures transform into
organizations less strongly dependent on any single individual.
Chapter 8 by Thierry Burger-Helmchen and Patrick Llerena is conceptual in character. With organizational learning, the chapter discusses a
crucial prerequisite of sustained competiveness and an important driver
of firm development. The authors’ point of departure is what may be
considered the economic essence of the firm: creating and capturing
value. Burger-Helmchen and Llerena suggest that creating value has an
individual as well as an organizational dimension. At the individual level,
creating value is linked to the creation of new knowledge, which in turn

is linked to interaction with other individuals (often other employees of
the same firm). At the organizational level, creating value involves finding
appropriate divisions of knowledge and labor both within and across the
boundaries of the organization.
Based on how knowledge and labor are divided, firms may differ in their
capacity to create value through exploration (acquiring new knowledge)
and through exploitation (using their existing knowledge). In turn, both
forms of organizational learning are favored by different conditions in
terms of network structures, trust, and specialization in knowledge about
the design of goods and services. To manage the alternative learning
processes, Burger-Helmchen and Llerena suggest a focus on creative individuals. These may be either internal or external to the firm organization,
and contractual relationships with them may be either of a more transactional or of a more relational character. The suitable form of contractual arrangement along these two dimensions depends on the value and
uniqueness of the respective individual’s human capital. Within the firm,
somewhat different implications for the suitable work structures, forms
of remuneration and learning opportunities follow from considerations
related to the divisions of labor and knowledge.

5

PART III: EVOLVING FIRMS AS DRIVERS OF
ECONOMIC DEVELOPMENT

Part III contains three chapters that are also concerned with evolving firms,
but emphasize the broader implications they have for the development of

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